Population-based models of morphological variability are useful for automating medical image processing tasks, diagnosis device and tool design, education and training as well as exploratory hypothesis formation in clinical research. We present a method to (a) generate such 3D models efficiently, (b) explore the morphological characteristics of the underlying population in an intuitive visual and quantitative manner, and (c) correlate clinical or functional parameters with morphological descriptors. With our approach one can synthesize new anatomically plausible shapes, based on basic and intuitive parameter queries. Our approach yields new insights into the correlation of morphological and clinical parameters within a given population. It allows to synthesize large numbers of representative new shapes, e.g. for data augmentation in artificial intelligence applications or large-scale functional analysis, e.g. via computational fluid dynamics simulation.